Nonnegative estimation and variable selection under minimax concave penalty for sparse high-dimensional linear regression models

Ning Li; Hu Yang

doi:10.1007/s00362-019-01107-w

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Nonnegative estimation and variable selection under minimax concave penalty for sparse high-dimensional linear regression models

Ning Li Hu Yang

Year: 2019 Journal: Statistical Papers Vol: 62 (2)Pages: 661-680 Publisher: Springer Science+Business Media

DOI: 10.1007/s00362-019-01107-w

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Keywords:

Mathematics Minimax Estimator Mathematical optimization Lasso (programming language) Penalty method Minimax estimator Feature selection Applied mathematics Minimum-variance unbiased estimator Computer science Statistics Artificial intelligence

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Statistical Methods and Inference

Physical Sciences → Mathematics → Statistics and Probability

Statistical Methods and Bayesian Inference

Physical Sciences → Mathematics → Statistics and Probability

Advanced Statistical Methods and Models

Physical Sciences → Mathematics → Statistics and Probability

Nonnegative estimation and variable selection under minimax concave penalty for sparse high-dimensional linear regression models

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